title: MRI-based attenuation correction for PET/MRI: a novel approach combining pattern recognition and atlas registration.
abstract: For quantitative PET information, correction of tissue photon attenuation is mandatory. Generally in conventional PET, the attenuation map is obtained from a transmission scan, which uses a rotating radionuclide source, or from the CT scan in a combined PET/CT scanner. In the case of PET/MRI scanners currently under development, insufficient space for the rotating source exists; the attenuation map can be calculated from the MR image instead. This task is challenging because MR intensities correlate with proton densities and tissue-relaxation properties, rather than with attenuation-related mass density.
  
  
  METHODS
  We used a combination of local pattern recognition and atlas registration, which captures global variation of anatomy, to predict pseudo-CT images from a given MR image. These pseudo-CT images were then used for attenuation correction, as the process would be performed in a PET/CT scanner.
  
  
  RESULTS
  For human brain scans, we show on a database of 17 MR/CT image pairs that our method reliably enables estimation of a pseudo-CT image from the MR image alone. On additional datasets of MRI/PET/CT triplets of human brain scans, we compare MRI-based attenuation correction with CT-based correction. Our approach enables PET quantification with a mean error of 3.2% for predefined regions of interest, which we found to be clinically not significant. However, our method is not specific to brain imaging, and we show promising initial results on 1 whole-body animal dataset.
  
  
  CONCLUSIONS
  This method allows reliable MRI-based attenuation correction for human brain scans. Further work is necessary to validate the method for whole-body imaging.
authors.affiliation: Laboratory for Preclinical Imaging and Imaging Technology of the Werner Siemens-Foundation, Department of Radiology, Eberhard-Karls-University, Tuebingen, Germany.
authors.forename: Matthias
authors.initials: M
authors.lastname: Hofmann
authors.forename: Florian
authors.initials: F
authors.lastname: Steinke
authors.forename: Verena
authors.initials: V
authors.lastname: Scheel
authors.forename: Guillaume
authors.initials: G
authors.lastname: Charpiat
authors.forename: Jason
authors.initials: J
authors.lastname: Farquhar
authors.forename: Philip
authors.initials: P
authors.lastname: Aschoff
authors.forename: Michael
authors.initials: M
authors.lastname: Brady
authors.forename: Bernhard
authors.initials: B
authors.lastname: Schölkopf
authors.forename: Bernd J
authors.initials: BJ
authors.lastname: Pichler
mesh_terms: D016477:Artifacts; D001921:Brain / Q000000981:diagnostic imaging*; D016208:Databases, Factual; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D049268:Positron-Emission Tomography / Q000379:methods*; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed; D051598:Whole Body Imaging